Subprime Mortgages and Home Equity Lines of Credit: Theoretical Underpinnings from the Demand-Side
Bibliographic record
Abstract
Subprime mortgages, HELOCs, supply-side restrictions, fraud and misrepresentation have been postulated as causes of the “housing bubble” in the U.S. in the early- to mid- 2000s. This paper offers a theoretical demand-side explanation instead. Utilizing a sunspot model of housing demand and home equity lending, it is shown how agent preferences generate sunspot equilibria which cause housing prices to be excessively volatile. It is also suggested how the Fed’s dramatic reductions, then increases in interest rates during the earlyto mid- 2000s, could have played a role in increasing housing price volatility. Finally, this paper shows how tax policy could be used to eliminate sunspots in the housing market. If this tax policy is not followed, housing price volatility could increase like in the U.S. and Japan (more than a decade earlier).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".